To evaluate the recommendation performance, we adopt two commonly used metrics: Root Mean Square Error (RMSE) and Mean Absolute Error (MAE).
As to explanation performance, we measure the generated explanations from two main perspectives: text quality and explainability.
For the former, we adopt BLEU [1]} in machine translation and ROUGE [2]} in text summarization, and report BLEU-1 and BLEU-4, and Precision, Recall and F1 of ROUGE-1 and ROUGE-2.
Though being widely used, BLUE and ROUGE are not flawless.
For example, it is difficult for them to detect the problem of identical sentences generated by Transformer.
These identical sentences might not be used as explanations, because they are less likely to well explain the special property of different recommendations.
To quantitatively measure how severe the problem is, we adopt USR that computes the Unique Sentence Ratio of generated sentences [3]}.
